Unsupervised Domain Adaptation: For a fair comparison with other works, we only use ResNet101 [1]}. Apart from weights for losses, all other hyper-parameters are the same as above. We search for \(\lambda _s \in \lbrace 0.5, 1.0\rbrace \)  and \(\lambda _{ent} \in \lbrace 0.05, 0.01, 0.1\rbrace \) . We use \(\lambda _{s}=0.5, \lambda _{ent} = 0.01, \lambda _{vat} = 0.01\)  for all our experiments when the corresponding losses are used. We use \(\lambda _{vat} = 0.01\) , without any tuning. Also, the parameters for the VAT [2]} loss are the same as those in the original work.
